Traditional onboarding asks users for information. A more powerful AI pattern is to take a single piece of data, like a URL or email access, immediately derive context, and show the user what the AI understands about them. This "show, don't tell" approach builds trust and demonstrates value instantly.

While the goal is to build a platform (second-order thinking), initial single-purpose app ideas (first-order) are critical. They serve as your "golden evaluation set"—a collection of core use cases that validate your platform is solving real user problems and is truly useful.

The best agentic UX isn't a generic chat overlay. Instead, identify where users struggle with complex inputs like formulas or code. Replace these friction points with a native, natural language interface that directly integrates the AI into the core product workflow, making it feel seamless and powerful.

Open-ended prompts overwhelm new users who don't know what's possible. A better approach is to productize AI into specific features. Use familiar UI like sliders and dropdowns to gather user intent, which then constructs a complex prompt behind the scenes, making powerful AI accessible without requiring prompt engineering skills.

For marketing, resist the allure of all-in-one AI platforms. The best results currently come from a specialized stack of hyper-focused tools, each excelling at a single task like image generation or presentation creation. Combine their outputs for superior quality.

The path to $50k MRR for a mobile app isn't a feature-rich platform. It's an obsessive focus on doing one job perfectly for a specific group with a recurring need. Examples include 'value this vinyl,' 'create this logo,' or 'summarize this text.'

Don't start with a broad market. Instead, find a niche group with a strong identity (e.g., collectors, churchgoers) that has a recurring, high-stakes problem needing an urgent solution. AI is particularly effective at solving these 'nerve' problems.